
# Set project root folder, to enable importing project files from subdirectories
from pathlib import Path
import os

path = Path().resolve()
root_path = str(path).split('notebooks')[0]
os.chdir(root_path)

# Force reload of project modules, sometimes necessary for Jupyter kernel
%load_ext autoreload
%autoreload 2

# Display framework versions for easy debugging
%pip show cadCAD
%pip show radcad

Name: cadCAD
Version: 0.4.23
Summary: cadCAD: a differential games based simulation software package for research, validation, and         Computer Aided Design of economic systems
Home-page: https://github.com/cadCAD-org/cadCAD
Author: Joshua E. Jodesty
Author-email: joshua@block.science
License: LICENSE.txt
Location: /home/bscholtz/workspace/reflexer/venv/lib/python3.8/site-packages
Requires: fn, funcy, pandas, pathos
Required-by: 
Note: you may need to restart the kernel to use updated packages.
Name: radcad
Version: 0.5.4
Summary: A cadCAD implementation, for dynamical systems modelling & simulation
Home-page: None
Author: Benjamin Scholtz
Author-email: ben@bitsofether.com
License: None
Location: /home/bscholtz/workspace/reflexer/venv/lib/python3.8/site-packages
Requires: tables, pathos, boto3, ray, pandas
Required-by: 
Note: you may need to restart the kernel to use updated packages.


# Import all shared dependencies and setup
from shared import *

import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
# import plotly.io as pio
# pio.renderers.default = "png"
from pprint import pprint


# Update dataframe display settings
pd.set_option('display.max_columns', 100)
pd.set_option('display.max_rows', 50)


# experiment_results = 'experiments/system_model_v3/experiment_controller_sweep/experiment_results.hdf5'
experiment_results = 'experiments/system_model_v3/experiment_shocks/experiment_results.hdf5'


experiment_results_keys = []
with pd.HDFStore(experiment_results) as store:
    experiment_results_keys = list(filter(lambda x: "results" in x, store.keys()))
    exceptions_keys = list(filter(lambda x: "exceptions" in x, store.keys()))


# A list of all experiment result keys
experiment_results_keys

['/processed_results_2021-02-08T19:20:25.053328',
 '/processed_results_2021-02-08T19:45:15.538863',
 '/processed_results_2021-02-08T20:05:35.827550',
 '/processed_results_2021-02-08T22:54:43.005952',
 '/processed_results_2021-02-08T23:20:54.834451',
 '/results_2021-02-08T19:11:14.829535',
 '/results_2021-02-08T19:20:25.053328',
 '/results_2021-02-08T19:45:15.538863',
 '/results_2021-02-08T20:05:35.827550',
 '/results_2021-02-08T22:54:43.005952',
 '/results_2021-02-08T23:20:54.834451',
 '/results_2021-02-09T09:43:01.154807']


# A list of all experiment result exception keys
exceptions_keys

['/exceptions_2021-02-08T19:11:14.829535',
 '/exceptions_2021-02-08T19:20:25.053328',
 '/exceptions_2021-02-08T19:45:15.538863',
 '/exceptions_2021-02-08T20:05:35.827550',
 '/exceptions_2021-02-08T22:54:43.005952',
 '/exceptions_2021-02-08T23:20:54.834451',
 '/exceptions_2021-02-09T09:43:01.154807']


# Copy a results_ key from the above keys to select the experiment
experiment_results_key = 'results_2021-02-09T09:43:01.154807' # Or select last result: experiment_results_keys[-1]
experiment_timestamp = experiment_results_key.strip('results_')
exceptions_key = 'exceptions_' + experiment_timestamp
experiment_timestamp

'2021-02-09T09:43:01.154807'


df_raw = pd.read_hdf(experiment_results, experiment_results_key)
df_raw.tail()


exceptions_df = pd.read_hdf(experiment_results, exceptions_key)
exceptions_df.head()


# Print the first 5 exceptions - indicating failed simulations
pprint(list(exceptions_df['exception'])[:5])

[FloatingPointError('underflow encountered in double_scalars'),
 FloatingPointError('underflow encountered in double_scalars'),
 FloatingPointError('underflow encountered in double_scalars'),
 FloatingPointError('underflow encountered in double_scalars'),
 FloatingPointError('underflow encountered in double_scalars')]


from experiments.system_model_v3.post_process import post_process_results
from experiments.system_model_v3.experiment_shocks import params, SIMULATION_TIMESTEPS

* Number of timesteps: 1440 / 60.0 days
* Timestep duration: 0.004 seconds
* Control parameters: ['kp', 'ki']
* Approx. number of values per parameter: 3
* Number of parameter combinations: 9
* Expected experiment duration: 0.8640000000000001 minutes / 0.014400000000000001 hours


df = post_process_results(df_raw, params, set_params=['ki', 'kp', 'liquidation_ratio'])
df


%%capture
# Save the processed results to the same HDF5 store file
df.to_hdf(experiment_results, key=f'processed_results_{experiment_timestamp}')


from radcad.core import generate_parameter_sweep

param_sweep = generate_parameter_sweep(params)


df_control_parameters = df[['subset', 'kp', 'ki']]

df_control_parameters = df_control_parameters.drop_duplicates(subset=['kp', 'ki'])
df_control_parameters


df.query('subset == 0')[['timestamp', 'eth_price', 'run']].plot(
    title="ETH price shocks (positive and negative step and impulse; one shock type for each run)",
    x='timestamp',
    y='eth_price', 
    color='run'
)


fig = px.line(
    df.query('run == 1'),
    title="Price response for all control parameter subsets, first run",
    x="timestamp",
    y=["market_price", "market_price_twap", "target_price_scaled"], 
    facet_col="ki", 
    facet_row="kp", 
    height=1000
)
fig.show()


initial_target_price = df['target_price'].iloc[0]
initial_target_price

3.14


df_market_price_zero = df.query("market_price <= 0.1*@initial_target_price")
df_market_price_zero[['subset', 'kp', 'ki']].drop_duplicates(subset=['kp', 'ki'])


df_market_price_infinity = df.query("market_price > 10*@initial_target_price")
df_market_price_infinity[['subset', 'kp', 'ki']].drop_duplicates(subset=['kp', 'ki'])


df['stable_price'] = False
df.loc[df.eval("""
0.1*@initial_target_price < market_price <= 10*@initial_target_price and 0.1*@initial_target_price < target_price_scaled <= 10*@initial_target_price
"""), 'stable_price'] = True
df_stable_price = df.groupby("subset").filter(lambda x: all(x.query('timestep > 24*2')['stable_price'])) #  and x['timestep'].max() == SIMULATION_TIMESTEPS
df_stable_price['subset'].unique()

array([0, 3, 4, 6, 7, 8])


fig = px.line(
    df_stable_price.query('run == 1'),
    title="Base case: Stable ETH price response",
    x="timestamp",
    y=["market_price", "market_price_twap", "target_price_scaled"],
    facet_col="kp",
    facet_row="ki",
    facet_col_wrap=2,
    height=1000
)
# fig.for_each_annotation(lambda a: a.update(text = f"kp={param_sweep[int(a.text.split('=')[-1])]['kp']} ki={param_sweep[int(a.text.split('=')[-1])]['ki']}"))
fig.show()


fig = px.line(
    df_stable_price.query('run == 2'),
    title="ETH price 30% step response",
    x="timestamp",
    y=["market_price", "market_price_twap", "target_price_scaled"],
    facet_col="kp",
    facet_row="ki",
    facet_col_wrap=2,
    height=1000
)
fig.show()


fig = px.line(
    df_stable_price.query('run == 3'),
    title="ETH price 30% impulse response",
    x="timestamp",
    y=["market_price", "market_price_twap", "target_price_scaled"],
    facet_col="kp",
    facet_row="ki",
    facet_col_wrap=2,
    height=1000
)
fig.show()


fig = px.line(
    df_stable_price.query('run == 4'),
    title="ETH price negative 30% step response",
    x="timestamp",
    y=["market_price", "market_price_twap", "target_price_scaled"],
    facet_col="kp",
    facet_row="ki",
    facet_col_wrap=2,
    height=1000
)
fig.show()


fig = px.line(
    df_stable_price.query('run == 5'),
    title="ETH price negative 30% impulse response",
    x="timestamp",
    y=["market_price", "market_price_twap", "target_price_scaled"],
    facet_col="kp",
    facet_row="ki",
    facet_col_wrap=2,
    height=1000
)
fig.show()


fig = px.line(
    df_stable_price, 
    title="Reflexer principal debt",
    x="timestamp", 
    y=["principal_debt"], 
    color='run', 
    facet_col="subset", 
    facet_col_wrap=4
)
fig.show()


fig = px.line(
    df_stable_price, 
    title="Secondary market RAI balance",
    x="timestamp", 
    y=["RAI_balance"], 
    color='run', 
    facet_col="subset", 
    facet_col_wrap=4
)
fig.show()


fig = px.line(
    df_stable_price, 
    title="Reflexer ETH collateral",
    x="timestamp", 
    y=["eth_collateral"], 
    color='run', 
    facet_col="subset", 
    facet_col_wrap=4
)
fig.show()


fig = px.line(
    df_stable_price, 
    title="Secondary market ETH balance",
    x="timestamp", 
    y=["ETH_balance"], 
    color='run', 
    facet_col="subset", 
    facet_col_wrap=4
)
fig.show()


df_stable_price.plot(
    x='timestamp', 
    y=['collateralization_ratio'], 
    title='Collateralization ratio', 
    facet_col="subset",
)

	cdp_metrics	optimal_values	sim_metrics	timedelta	cumulative_time	timestamp	eth_price	liquidity_demand_mean	cdps	eth_collateral	eth_locked	eth_freed	principal_debt	rai_drawn	rai_wiped	accrued_interest	w_1	stability_fee	market_price	market_price_twap	target_price	target_rate	eth_gross_return	expected_market_price	expected_debt_price	error_star	error_star_integral	RAI_balance	ETH_balance	UNI_supply	uniswap_oracle	subset	run	substep	timestep	events
769414	{'cdp_count': 1, 'open_cdp_count': 1, 'closed_...	{}	{}	3600	3589200	2017-02-11 13:00:00	300.0	7.466109e-301	open time locked drawn ...	66110.592512	154827.528922	88716.936409	1.584718e+07	2.684323e+07	1.099606e+07	7887.068526	0.000165	1.585490e-10	1.283732	1.284012	0.863091	-5.068893e-09	1.0	1.32113	3.14	-0.032507	-34660.235438	1.584747e+07	67810.507448	10000000.0	<models.system_model_v3.model.parts.uniswap_or...	8	5	14	997	NaN
769415	{'cdp_count': 1, 'open_cdp_count': 1, 'closed_...	{}	{}	3600	3589200	2017-02-11 13:00:00	300.0	7.466109e-301	open time locked drawn ...	66110.592512	154827.528922	88716.936409	1.584718e+07	2.684352e+07	1.099606e+07	7887.068526	0.000165	1.585490e-10	1.283732	1.284012	0.863091	-5.068893e-09	1.0	1.32113	3.14	-0.032507	-34660.235438	1.584747e+07	67810.507448	10000000.0	<models.system_model_v3.model.parts.uniswap_or...	8	5	15	997	NaN
769416	{'cdp_count': 1, 'open_cdp_count': 1, 'closed_...	{}	{}	3600	3589200	2017-02-11 13:00:00	300.0	7.466109e-301	open time locked drawn ...	66110.592512	154827.528922	88716.936409	1.584747e+07	2.684352e+07	1.099606e+07	7887.068526	0.000165	1.585490e-10	1.283732	1.284012	0.863091	-5.068893e-09	1.0	1.32113	3.14	-0.032507	-34660.235438	1.584747e+07	67810.507448	10000000.0	<models.system_model_v3.model.parts.uniswap_or...	8	5	16	997	NaN
769417	{'cdp_count': 1, 'open_cdp_count': 1, 'closed_...	{}	{}	3600	3589200	2017-02-11 13:00:00	300.0	7.466109e-301	open time locked drawn ...	66110.592512	154827.528922	88716.936409	1.584747e+07	2.684352e+07	1.099606e+07	7887.068526	0.000165	1.585490e-10	1.283732	1.284012	0.863091	-5.068893e-09	1.0	1.32113	3.14	-0.032507	-34660.235438	1.584747e+07	67810.507448	10000000.0	<models.system_model_v3.model.parts.uniswap_or...	8	5	17	997	NaN
769418	{'cdp_count': 1, 'open_cdp_count': 1, 'closed_...	{}	{}	3600	3589200	2017-02-11 13:00:00	300.0	7.466109e-301	open time locked drawn ...	66110.592512	154827.528922	88716.936409	1.584747e+07	2.684352e+07	1.099606e+07	7887.068526	0.000165	1.585490e-10	1.283732	1.284012	0.863091	-5.068893e-09	1.0	1.32113	3.14	-0.032507	-34660.235438	1.584747e+07	67810.507448	10000000.0	<models.system_model_v3.model.parts.uniswap_or...	8	5	18	997	NaN

	exception	traceback	subset	timesteps	parameters	initial_state
0	underflow encountered in double_scalars	Traceback (most recent call last):\n File "/h...	0	1440	{"0":{"debug":false,"raise_on_assert":true,"fr...	{'cdp_metrics': {}, 'optimal_values': {}, 'sim...
1	underflow encountered in double_scalars	Traceback (most recent call last):\n File "/h...	1	1440	{"0":{"debug":false,"raise_on_assert":true,"fr...	{'cdp_metrics': {}, 'optimal_values': {}, 'sim...
2	underflow encountered in double_scalars	Traceback (most recent call last):\n File "/h...	2	1440	{"0":{"debug":false,"raise_on_assert":true,"fr...	{'cdp_metrics': {}, 'optimal_values': {}, 'sim...
3	underflow encountered in double_scalars	Traceback (most recent call last):\n File "/h...	3	1440	{"0":{"debug":false,"raise_on_assert":true,"fr...	{'cdp_metrics': {}, 'optimal_values': {}, 'sim...
4	underflow encountered in double_scalars	Traceback (most recent call last):\n File "/h...	4	1440	{"0":{"debug":false,"raise_on_assert":true,"fr...	{'cdp_metrics': {}, 'optimal_values': {}, 'sim...

	index	cdp_metrics	optimal_values	sim_metrics	timedelta	cumulative_time	timestamp	blockheight	eth_price	liquidity_demand	liquidity_demand_mean	cdps	eth_collateral	eth_locked	eth_freed	eth_bitten	principal_debt	rai_drawn	rai_wiped	rai_bitten	accrued_interest	interest_dripped	interest_wiped	interest_bitten	w_1	w_2	w_3	system_revenue	stability_fee	market_price	market_price_twap	target_price	target_rate	eth_return	eth_gross_return	expected_market_price	expected_debt_price	error_star	error_star_integral	secondary_market_slippage	RAI_balance	ETH_balance	UNI_supply	uniswap_oracle	simulation	subset	run	substep	timestep	events	eth_collateral_value	collateralization_ratio	ki	kp	liquidation_ratio	target_price_scaled
0	0	{}	{}	{}	0	0	2017-01-01 00:00:00	0	294.069151	1.0	1.000000e+00	None	154827.528922	154827.528922	0.000000	0.0	1.000000e+07	1.000000e+07	0.000000e+00	0.0	0.000000	0	0	0	0.000000e+00	0.0	0.0	0.0	1.585490e-10	3.140000	0.000000	3.140000	0.000000e+00	0.000000	0.0	3.140000	3.14	0.000000	0.000000	0	1.000000e+07	106777.606153	10000000.0	None	0	0	1	0	0	NaN	4.553000e+07	1.450000	-1.000000e-09	1.000000e-07	1.45	4.553000
1	18	{'cdp_count': 1, 'open_cdp_count': 1, 'closed_...	{}	{}	3600	3600	2017-01-01 01:00:00	0	300.000000	0.0	5.000000e-01	None	154827.528922	154827.528922	0.000000	0.0	1.450000e+07	1.450000e+07	0.000000e+00	0.0	5.707764	0	0	0	8.276258e+00	0.0	0.0	0.0	1.585490e-10	3.140000	0.000000	2.165517	0.000000e+00	0.020168	1.0	3.231683	3.14	3.140000	5652.000000	0	1.450000e+07	73708.353400	10000000.0	None	0	0	1	18	1	NaN	4.644826e+07	1.479244	-1.000000e-09	1.000000e-07	1.45	3.140000
2	36	{'cdp_count': 1, 'open_cdp_count': 1, 'closed_...	{}	{}	3600	7200	2017-01-01 02:00:00	0	300.000000	-0.0	2.500000e-01	None	105925.122117	154827.528922	48902.406805	0.0	1.012023e+07	1.450000e+07	4.379765e+06	0.0	11.484158	0	0	0	4.723892e-06	0.0	0.0	0.0	1.585490e-10	1.525000	0.000000	2.165517	0.000000e+00	0.000000	1.0	1.569093	3.14	3.140000	16932.000000	0	1.012023e+07	105703.331685	10000000.0	None	0	0	1	18	2	NaN	3.177754e+07	1.450000	-1.000000e-09	1.000000e-07	1.45	3.140000
3	54	{'cdp_count': 1, 'open_cdp_count': 1, 'closed_...	{}	{}	3600	10800	2017-01-01 03:00:00	0	300.000000	0.0	1.250000e-01	None	105925.122117	154827.528922	48902.406805	0.0	1.012023e+07	1.450000e+07	4.379765e+06	0.0	17.260556	0	0	0	2.696510e-12	0.0	0.0	0.0	1.585490e-10	3.133425	0.000000	2.165517	0.000000e+00	0.000000	1.0	3.224303	3.14	3.140000	28166.000000	0	1.012023e+07	105703.331685	10000000.0	None	0	0	1	18	3	NaN	3.177754e+07	1.450000	-1.000000e-09	1.000000e-07	1.45	3.140000
4	72	{'cdp_count': 1, 'open_cdp_count': 1, 'closed_...	{}	{}	3600	14400	2017-01-01 04:00:00	0	300.000000	-0.0	6.250000e-02	None	105925.122117	154827.528922	48902.406805	0.0	1.012023e+07	1.450000e+07	4.379765e+06	0.0	23.036956	0	0	0	0.000000e+00	0.0	0.0	0.0	1.585490e-10	3.133425	0.000000	2.165517	0.000000e+00	0.000000	1.0	3.224444	3.14	3.140000	39354.000000	0	1.012023e+07	105703.331685	10000000.0	None	0	0	1	18	4	NaN	3.177754e+07	1.450000	-1.000000e-09	1.000000e-07	1.45	3.140000
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
42783	769346	{'cdp_count': 1, 'open_cdp_count': 1, 'closed_...	{}	{}	3600	3574800	2017-02-11 09:00:00	0	300.000000	0.0	1.194577e-299	None	66110.592512	154827.528922	88716.936409	0.0	1.584631e+07	2.684237e+07	1.099606e+07	0.0	7850.870783	0	0	0	1.645057e-04	0.0	0.0	0.0	1.585490e-10	1.283918	1.284291	0.863154	-5.059519e-09	0.000000	1.0	1.321322	3.14	-0.032695	-34764.662660	0	1.584631e+07	67815.435562	10000000.0	None	0	8	5	18	993	NaN	1.983318e+07	1.450026	-1.000000e-07	1.000000e-05	1.45	1.251573
42784	769364	{'cdp_count': 1, 'open_cdp_count': 1, 'closed_...	{}	{}	3600	3578400	2017-02-11 10:00:00	0	300.000000	0.0	5.972887e-300	None	66110.592512	154827.528922	88716.936409	0.0	1.584660e+07	2.684266e+07	1.099606e+07	0.0	7859.919964	0	0	0	1.647442e-04	0.0	0.0	0.0	1.585490e-10	1.283872	1.284151	0.863138	-5.059519e-09	0.000000	1.0	1.321274	3.14	-0.032578	-34738.492828	0	1.584660e+07	67814.204070	10000000.0	None	0	8	5	18	994	NaN	1.983318e+07	1.450026	-1.000000e-07	1.000000e-05	1.45	1.251550
42785	769382	{'cdp_count': 1, 'open_cdp_count': 1, 'closed_...	{}	{}	3600	3582000	2017-02-11 11:00:00	0	300.000000	-0.0	2.986444e-300	None	66110.592512	154827.528922	88716.936409	0.0	1.584689e+07	2.684294e+07	1.099606e+07	0.0	7868.969315	0	0	0	1.647472e-04	0.0	0.0	0.0	1.585490e-10	1.283825	1.284151	0.863122	-5.059519e-09	0.000000	1.0	1.321226	3.14	-0.032601	-34712.323053	0	1.584689e+07	67812.972601	10000000.0	None	0	8	5	18	995	NaN	1.983318e+07	1.450026	-1.000000e-07	1.000000e-05	1.45	1.251528
42786	769400	{'cdp_count': 1, 'open_cdp_count': 1, 'closed_...	{}	{}	3600	3585600	2017-02-11 12:00:00	0	300.000000	0.0	1.493222e-300	None	66110.592512	154827.528922	88716.936409	0.0	1.584718e+07	2.684323e+07	1.099606e+07	0.0	7878.018836	0	0	0	1.647502e-04	0.0	0.0	0.0	1.585490e-10	1.283778	1.284151	0.863107	-5.068893e-09	0.000000	1.0	1.321178	3.14	-0.032624	-34686.405119	0	1.584718e+07	67811.741154	10000000.0	None	0	8	5	18	996	NaN	1.983318e+07	1.450026	-1.000000e-07	1.000000e-05	1.45	1.251505
42787	769418	{'cdp_count': 1, 'open_cdp_count': 1, 'closed_...	{}	{}	3600	3589200	2017-02-11 13:00:00	0	300.000000	0.0	7.466109e-301	open time locked drawn ...	66110.592512	154827.528922	88716.936409	0.0	1.584747e+07	2.684352e+07	1.099606e+07	0.0	7887.068526	0	0	0	1.650584e-04	0.0	0.0	0.0	1.585490e-10	1.283732	1.284012	0.863091	-5.068893e-09	0.000000	1.0	1.321130	3.14	-0.032507	-34660.235438	0	1.584747e+07	67810.507448	10000000.0	<models.system_model_v3.model.parts.uniswap_or...	0	8	5	18	997	NaN	1.983318e+07	1.450026	-1.000000e-07	1.000000e-05	1.45	1.251482

	subset	kp	ki
0	0	1.000000e-07	-1.000000e-09
998	1	1.000000e-07	-1.000000e-08
1996	2	1.000000e-07	-1.000000e-07
2994	3	1.000000e-06	-1.000000e-09
3992	4	1.000000e-06	-1.000000e-08
4990	5	1.000000e-06	-1.000000e-07
5988	6	1.000000e-05	-1.000000e-09
6986	7	1.000000e-05	-1.000000e-08
7984	8	1.000000e-05	-1.000000e-07

	subset	kp	ki
19809	1	1.000000e-07	-1.000000e-08
20447	2	1.000000e-07	-1.000000e-07
23068	5	1.000000e-06	-1.000000e-07

Experiment Analysis: Controller parameter stability search¶

Setup and Dependencies¶

Load Results¶

Post Process Results¶

Control Parameters¶

Simulation Analysis¶